Non-parametric Power Spectrum Estimation with Circular Overlap

This paper studies non-parametric power spectrum estimation with circular overlap. Although circular overlap imposes a discontinuity on the stochastic process under investigation; it is shown that for a (Gaussian) ergodic weakly stationary process the power spectrum estimator with circular overlap is asymptotically unbiased. Also the variance decreases due to the overlap. Furthermore, a closed form expression is derived for the lowest reachable variance, with respect to all possible fractions of overlap.